



'''simple docstring'''



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_  :							List[Any]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							Optional[int]             =    {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Dict						  =					'pix2struct_text_model'
						_lowerCAmelCase  : List[str]						  =					['past_key_values']
						_lowerCAmelCase  : str						  =					{
						    'hidden_size': 'hidden_size',
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(	self :   List[Any]					,     lowerCAmelCase__ :   int=5_02_44					,     lowerCAmelCase__ :   Dict=7_68					,     lowerCAmelCase__ :   Dict=64					,     lowerCAmelCase__ :   int=20_48					,     lowerCAmelCase__ :   Optional[Any]=12					,     lowerCAmelCase__ :   Tuple=12					,     lowerCAmelCase__ :   Union[str, Any]=32					,     lowerCAmelCase__ :   List[Any]=1_28					,     lowerCAmelCase__ :   List[str]=0.1					,     lowerCAmelCase__ :   Optional[Any]=1e-6					,     lowerCAmelCase__ :   Optional[int]=1.0					,     lowerCAmelCase__ :   Dict="gelu_new"					,     lowerCAmelCase__ :   str=0					,     lowerCAmelCase__ :   int=False					,     lowerCAmelCase__ :   List[Any]=0					,     lowerCAmelCase__ :   Optional[Any]=1					,     lowerCAmelCase__ :   str=False					,     lowerCAmelCase__ :   Dict=True					,     **lowerCAmelCase__ :   List[Any]					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      vocab_size
													SCREAMING_SNAKE_CASE       :   Tuple         =      hidden_size
													SCREAMING_SNAKE_CASE       :   int         =      d_kv
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      d_ff
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      num_layers
													SCREAMING_SNAKE_CASE       :   int         =      num_heads
													SCREAMING_SNAKE_CASE       :   List[str]         =      relative_attention_num_buckets
													SCREAMING_SNAKE_CASE       :   Dict         =      relative_attention_max_distance
													SCREAMING_SNAKE_CASE       :   List[Any]         =      dropout_rate
													SCREAMING_SNAKE_CASE       :   List[str]         =      layer_norm_epsilon
													SCREAMING_SNAKE_CASE       :   str         =      initializer_factor
													SCREAMING_SNAKE_CASE       :   Tuple         =      use_cache

													SCREAMING_SNAKE_CASE       :   List[Any]         =      eos_token_id
													SCREAMING_SNAKE_CASE       :   List[str]         =      decoder_start_token_id

													# for backwards compatibility
													SCREAMING_SNAKE_CASE       :   List[str]         =      dense_act_fn

													super().__init__(
													    pad_token_id=lowerCAmelCase__					,     eos_token_id=lowerCAmelCase__					,     decoder_start_token_id=lowerCAmelCase__					,     tie_word_embeddings=lowerCAmelCase__					,     is_decoder=lowerCAmelCase__					,     **lowerCAmelCase__					,     )







						@classmethod
						def        __lowercase     (	cls :   int					,     lowerCAmelCase__ :   Union[str, os.PathLike]					,     **lowerCAmelCase__ :   int					):

													"""simple docstring"""
													cls._set_token_in_kwargs(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[Any]         =      cls.get_config_dict(lowerCAmelCase__					,     **lowerCAmelCase__					)

													# get the text config dict if we are loading from Pix2StructConfig
													if config_dict.get('''model_type'''					) == "pix2struct":
																				SCREAMING_SNAKE_CASE       :   Dict         =      config_dict['''text_config''']

													if "model_type" in config_dict and hasattr(cls					,     '''model_type'''					) and config_dict["model_type"] != cls.model_type:
																				logger.warning(
																				    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																				    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

													return cls.from_dict(lowerCAmelCase__					,     **lowerCAmelCase__					)


class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Dict						  =					'pix2struct_vision_model'







						def __init__(	self :   List[Any]					,     lowerCAmelCase__ :   Union[str, Any]=7_68					,     lowerCAmelCase__ :   List[Any]=7_68					,     lowerCAmelCase__ :   List[Any]=20_48					,     lowerCAmelCase__ :   str=64					,     lowerCAmelCase__ :   str=12					,     lowerCAmelCase__ :   List[Any]=12					,     lowerCAmelCase__ :   List[Any]="gelu_new"					,     lowerCAmelCase__ :   Union[str, Any]=1e-6					,     lowerCAmelCase__ :   Union[str, Any]=0.0					,     lowerCAmelCase__ :   Tuple=0.0					,     lowerCAmelCase__ :   int=1e-10					,     lowerCAmelCase__ :   Union[str, Any]=1.0					,     lowerCAmelCase__ :   str=40_96					,     lowerCAmelCase__ :   Union[str, Any]=32					,     lowerCAmelCase__ :   Optional[Any]=1_28					,     **lowerCAmelCase__ :   List[Any]					,     ):

													"""simple docstring"""
													super().__init__(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   int         =      hidden_size
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      patch_embed_hidden_size
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      d_ff
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      dropout_rate
													SCREAMING_SNAKE_CASE       :   int         =      num_hidden_layers
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      num_attention_heads
													SCREAMING_SNAKE_CASE       :   Dict         =      initializer_range
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      initializer_factor
													SCREAMING_SNAKE_CASE       :   str         =      attention_dropout
													SCREAMING_SNAKE_CASE       :   int         =      layer_norm_eps
													SCREAMING_SNAKE_CASE       :   Dict         =      dense_act_fn
													SCREAMING_SNAKE_CASE       :   List[str]         =      seq_len
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      relative_attention_num_buckets
													SCREAMING_SNAKE_CASE       :   List[Any]         =      relative_attention_max_distance
													SCREAMING_SNAKE_CASE       :   List[str]         =      d_kv







						@classmethod
						def        __lowercase     (	cls :   Any					,     lowerCAmelCase__ :   Union[str, os.PathLike]					,     **lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													cls._set_token_in_kwargs(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      cls.get_config_dict(lowerCAmelCase__					,     **lowerCAmelCase__					)

													# get the vision config dict if we are loading from Pix2StructConfig
													if config_dict.get('''model_type'''					) == "pix2struct":
																				SCREAMING_SNAKE_CASE       :   Any         =      config_dict['''vision_config''']

													if "model_type" in config_dict and hasattr(cls					,     '''model_type'''					) and config_dict["model_type"] != cls.model_type:
																				logger.warning(
																				    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																				    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""					)

													return cls.from_dict(lowerCAmelCase__					,     **lowerCAmelCase__					)


class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : List[Any]						  =					'pix2struct'
						_lowerCAmelCase  : int						  =					True







						def __init__(	self :   Dict					,     lowerCAmelCase__ :   Optional[int]=None					,     lowerCAmelCase__ :   Tuple=None					,     lowerCAmelCase__ :   Any=1.0					,     lowerCAmelCase__ :   Optional[Any]=0.02					,     lowerCAmelCase__ :   Any=False					,     lowerCAmelCase__ :   int=False					,     lowerCAmelCase__ :   Optional[Any]=True					,     **lowerCAmelCase__ :   List[Any]					,     ):

													"""simple docstring"""
													super().__init__(tie_word_embeddings=lowerCAmelCase__					,     is_encoder_decoder=lowerCAmelCase__					,     **lowerCAmelCase__					)

													if text_config is None:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      {}
																				logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''					)

													if vision_config is None:
																				SCREAMING_SNAKE_CASE       :   int         =      {}
																				logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''					)

													SCREAMING_SNAKE_CASE       :   str         =      PixaStructTextConfig(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   str         =      PixaStructVisionConfig(**lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      self.text_config.decoder_start_token_id
													SCREAMING_SNAKE_CASE       :   int         =      self.text_config.pad_token_id
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.text_config.eos_token_id

													SCREAMING_SNAKE_CASE       :   str         =      initializer_factor
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      initializer_range

													SCREAMING_SNAKE_CASE       :   Dict         =      self.initializer_range
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.initializer_range

													SCREAMING_SNAKE_CASE       :   List[str]         =      is_vqa







						@classmethod
						def        __lowercase     (	cls :   Union[str, Any]					,     lowerCAmelCase__ :   PixaStructTextConfig					,     lowerCAmelCase__ :   PixaStructVisionConfig					,     **lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													return cls(text_config=text_config.to_dict()					,     vision_config=vision_config.to_dict()					,     **lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      copy.deepcopy(self.__dict__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.text_config.to_dict()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      self.vision_config.to_dict()
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.__class__.model_type
													return output









'''simple docstring'''



from __future__ import annotations
def        UpperCAmelCase (    A	:					list[int]       ,		A	:					int					):
							if len(A					) < k or k < 0:
														raise ValueError('''Invalid Input'''					)
							SCREAMING_SNAKE_CASE       :   Dict         =      sum(array[:k]					)
							for i in range(len(A					) - k					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      current_sum - array[i] + array[i + k]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      max(A       ,		A					)
							return max_sum


if __name__ == "__main__":
							from doctest import testmod
							from random import randint

							testmod()
							lowerCAmelCase_  :							Optional[int]             =    [randint(-1000, 1000) for i in range(100)]
							lowerCAmelCase_  :							Optional[int]             =    randint(0, 110)
							print(f'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')




